Mapping schistosomiasis risk landscapes and implications for disease control: A case study for low endemic areas in the Middle Paranapanema river basin, São Paulo, Brazil

Background Schistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry o...

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Bibliographic Details
Published in:	PLoS neglected tropical diseases Vol. 18; no. 11; p. e0012582
Main Authors:	Ferreira da Silva, Vivian Alessandra, Kampel, Milton, Silva dos Anjos, Rafael, Gardini Sanches Palasio, Raquel, Escada, Maria Isabel Sobral, Tuan, Roseli, Singleton, Alyson, Glidden, Caroline Kate, Chamberlin, Andrew, De Leo, Giulio Alessandro, Pinter dos Santos, Adriano, Vieira Monteiro, Antônio Miguel
Format:	Journal Article
Language:	English
Published:	San Francisco, CA USA Public Library of Science 01-11-2024 Public Library of Science (PLoS)
Subjects:	Biology and Life Sciences Earth Sciences Ecology and Environmental Sciences Medicine and Health Sciences People and places Social Sciences
Online Access:	Get full text
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Summary:	Background Schistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas. Methodology/Principal findings In the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential for S. mansoni infection. Landscape connectivity metrics were also analysed. Conclusions/Significance This study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region’s low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination.
Bibliography:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work. The authors have declared that no competing interests exist.
ISSN:	1935-2735 1935-2727 1935-2735
DOI:	10.1371/journal.pntd.0012582